The ability to predict an aircraft's trajectory is useful for several reasons.
Air traffic management (ATM) would benefit from an improved ability to predict an aircraft's trajectory. Air traffic management is responsible for the safe separation of aircraft, a particularly demanding task in congested airspace such as around airports. ATM decision-support tools based on accurate trajectory predictions could allow a greater volume of aircraft to be handled while maintaining safety. By trajectory, a four-dimensional description of the aircraft's path is meant. The description may be the evolution of the aircraft's state with time, where the state may include the position of the aircraft's centre of mass and other aspects of its motion such as velocity, attitude and weight. This benefit is particularly significant where ATM is operating in and around airports. As demand for slots at airports increases, ATM is under constant pressure to increase capacity by decreasing separation between aircraft: increased accuracy in predicting aircraft trajectories enables this to be done without compromising safety. Also, greater predictability in aircraft trajectories allows arrival times to be determined more accurately thereby enabling better coordination with ground operations.
In current ATM practice, aircraft must typically fly set routes. For example, when approaching and departing an airport, aircraft are usually requested to fly a STAR (Standard Terminal Arrival Route) and a SID (Standard Instrument Departure), respectively. However, aircraft operators are requesting additional flexibility to fly according to their preferences, so that they can better pursue their business objectives. Furthermore, there is an increasing pressure on the ATM system to facilitate the reduction of the environmental impact of aircraft operations. As a result of the above, the ATM system requires the capability to predict operator-preferred trajectories as well as trajectories that minimize the impact on the environment, chiefly in terms of noise and emissions. In addition, the ATM system must be able to exchange descriptions of such trajectories with the operators in order to arrive at a coordinated, conflict-free solution to the traffic problem.
The ability to predict an aircraft's trajectory will also be of benefit to the management of autonomous vehicles such as unmanned air vehicles (UAVs), for example in programming flight plans for UAVs as well as in commanding and de-conflicting their trajectories.
In order to predict aircraft trajectory unambiguously, one must solve a set of differential equations that model both aircraft behaviour and atmospheric conditions. The computation process requires inputs corresponding to the aircraft intent.
Aircraft intent must be distinguished from flight intent. Flight intent may be thought of as a generalisation of the concept of a flight plan, and so will reflect operational requirements such as intended route and operator preferences. Generally, flight intent will not unambiguously define an aircraft's trajectory. Put another way, there are likely to be many aircraft trajectories that would satisfy a given flight intent. Thus, flight intent may be regarded as a basic blueprint in which the specific details required to compute unambiguously a trajectory are missing.
For example, the instructions to be followed during a STAR or a SID would correspond to an example of flight intent. In addition, airline preferences may also form an example of flight intent. To determine aircraft intent, instances of flight intent like a SID procedure, the airline's operational preferences and the actual pilot's decision making process must be combined. This is because aircraft intent comprises a structured set of instructions that are used by a trajectory computation infrastructure to provide an unambiguous trajectory. The instructions should include configuration details of the aircraft (e.g. landing gear deployment), and procedures to be followed during manoeuvres and normal flight (e.g. track a certain turn radius or hold a given airspeed). These instructions capture the basic commands and guidance modes at the disposal of the pilot and the aircraft's flight management system to direct the operation of the aircraft. Thus, aircraft intent may be thought of as an abstraction of the way in which an aircraft is commanded to behave by the pilot and/or flight management system. Of course, the pilot's decision making process is influenced by required procedures, for example as required to follow a STAR/SID or to comply with airline operational procedures.
Aircraft intent is expressed using a set of parameters presented so as to allow equations of motion to be solved. The theory of formal languages may be used to implement this formulation: an aircraft intent description language provides the set of instructions and the rules that govern the allowable combinations that express the aircraft intent, and so allow a prediction of the aircraft trajectory.
FIG. 1 shows a common infrastructure used in such aircraft trajectory computation, namely a trajectory computation infrastructure or TCI. The computation is executed by a trajectory engine. The trajectory engine requires as inputs both the aircraft intent description described above and also the initial state of the aircraft. The trajectory engine provides as an output a description of the computed trajectory for the aircraft. To produce such an output, the trajectory engine uses two models: an aircraft performance model and an Earth model.
The aircraft performance model provides the values of the aircraft performance aspects required by the trajectory engine to integrate the equations of motion. These values depend on the aircraft type for which the trajectory is being computed, the aircraft's current motion state (position, velocity, weight, etc) and the current local atmospheric conditions. In addition, the performance values may depend on the intended operation of the aircraft, i.e. on the aircraft intent. For example, a trajectory engine may use the aircraft performance model to provide a value of the instantaneous rate of descent corresponding to a certain aircraft weight, atmospheric conditions (pressure altitude and temperature) and intended speed schedule (e.g. constant calibrated airspeed). The trajectory engine will also request from the aircraft performance model the values of the applicable limitations so as to ensure that the aircraft motion remains within the flight envelope. The aircraft performance model is also responsible for providing the TE with other performance-related aspects that are intrinsic to the aircraft, such as flap and landing gear deployment times.
The Earth model provides information relating to environmental conditions, such as the state of the atmosphere, weather conditions, gravity and magnetic variation.
The trajectory engine uses the inputs, the aircraft performance model and the Earth model to solve a set of equations of motion. Many different sets of equations of motion are available that vary in complexity, and that may reduce the aircraft's motion to fewer degrees of freedom by means of a certain set of simplifying assumptions.
The trajectory computation infrastructure may be air-based or land-based. For example, the trajectory computation infrastructure may be associated with an aircraft's flight management system that controls the aircraft on the basis of a predicted trajectory that captures the airline operating preferences and business objectives. The primary role for land-based trajectory computation infrastructures is for air traffic management.
For land-based systems, the output of the trajectory engine (i.e. the description of the computed trajectory) is provided to an application that provides a service to a particular ATM body or organisation. However, many different such applications exist, with each ATM application using its own trajectory modelling. To date, there has been little, if any, commonality between these applications. Given the anticipated growth in the number and sophistication of such trajectory-based ATM applications, this lack of commonality is a serious issue since, for safety reasons: different applications dealing with the same flight must hold consistent predictions for the trajectory of that flight.
In addition, the accuracy of these applications has been limited. This situation may be attributed to several factors including scarce availability of aircraft performance data, limited computing power, less stringent requirements for accuracy and lack of coordination initiatives.
There is also a need to ensure that the trajectories predicted by ground-based ATM tools can be synchronized with those predicted by an aircraft's flight management system. As noted above, the aircraft's flight management system controls the aircraft on the basis of a predicted trajectory that captures the airline operating preferences and business objectives. The amendments to this reference business trajectory coming from the ground must be made in a way that is consistent with the flight management system's trajectory modelling methodology to ensure that air and ground systems operate in a coordinated manner.